[2603.25068] Ultra-fast Traffic Nowcasting and Control via Differentiable Agent-based Simulation

Computer Science > Multiagent Systems arXiv:2603.25068 (cs) [Submitted on 26 Mar 2026] Title:Ultra-fast Traffic Nowcasting and Control via Differentiable Agent-based Simulation Authors:Fumiyasu Makinoshima, Yuya Yamaguchi, Eigo Segawa, Koichiro Niinuma, Sean Qian View a PDF of the paper titled Ultra-fast Traffic Nowcasting and Control via Differentiable Agent-based Simulation, by Fumiyasu Makinoshima and 4 other authors View PDF HTML (experimental) Abstract:Traffic digital twins, which inform policymakers of effective interventions based on large-scale, high-fidelity computational models calibrated to real-world traffic, hold promise for addressing societal challenges in our rapidly urbanizing world. However, conventional fine-grained traffic simulations are non-differentiable and typically rely on inefficient gradient-free optimization, making calibration for real-world applications computationally infeasible. Here we present a differentiable agent-based traffic simulator that enables ultra-fast model calibration, traffic nowcasting, and control on large-scale networks. We develop several differentiable computing techniques for simulating individual vehicle movements, including stochastic decision-making and inter-agent interactions, while ensuring that entire simulation trajectories remain end-to-end differentiable for efficient gradient-based optimization. On the large-scale Chicago road network, with over 10,000 calibration parameters, our model simulates more than one...